This supplement describes the laboratory preparation, analysis, and quantitative analysis of plant microfossils recovered from the Bargny 1 deposits.
Sediment samples were ground, passed through a 250-micron sieve, and placed in a shaker overnight with Calgon solution (sodium hexametaphosphate) before having sands and clays separated by settling and centrifugation-decant cycles at 2500 rpm for 2 minutes. At this point, samples were spiked with Lycopodium spores and treated with 10% HCl in a 40º bath for 10 minutes. After centrifugation-decant cycles until pH neutral, the samples were separated by density using a solution of zinc bromide and 5% HCl with a specific gravity of 2.3 g/ml. The resulting residue was extracted in ethanol and transferred to glycerol for analysis.
This set of laboratory methods for extracting phytoliths employs standard steps for digesting terrestrial sediments and recovering plant microfossils, but omits the use of a strong oxidizing agent such as peroxide or nitric acid. Judging from the well-oxidized state of the sediments, futher degradation of the organic remains would only remove valuable information represented by organic microfossils (pollen microfossils, aeomebas, etc.), but also permits the use of Lycopodium spores to track laboratory errors and to calculate microfossil concentrations.
| depth | stratum | LYCO_Count | LYCO_n | TODAL_diag | |
|---|---|---|---|---|---|
| BG1.020 | 20 | I | 19855 | 239 | 151 |
| BG1.040 | 40 | II | 20848 | 212 | 64 |
| BG1.060 | 60 | II | 20848 | 57 | 227 |
| BG1.090 | 90 | II | 19855 | 100 | 16 |
| BG1.100 | 100 | III | 14285 | 200 | 29 |
| BG1.110 | 110 | III | 19855 | 360 | 203 |
| BG1.150 | 150 | III | 14285 | 423 | 256 |
| BG1.175 | 175 | III | 19855 | 165 | 210 |
| BG1.195 | 195 | III | 14285 | 344 | 208 |
| BG1.210 | 210 | III | 19855 | 20 | 215 |
| BG1.220 | 220 | IV | 14285 | 134 | 223 |
| BG1.230 | 230 | IV | 14285 | 244 | 67 |
| BG1.240 | 240 | IV | 14285 | 287 | 200 |
| BG1.255 | 255 | V | 19855 | 163 | 374 |
| BG1.270 | 270 | V | 20848 | 20 | 226 |
| BG1.280 | 280 | V | 20848 | 11 | 231 |
| BG1.290 | 290 | V | 19855 | 66 | 301 |
| BG1.295 | 295 | V | 19855 | 78 | 231 |
| BG1.300 | 300 | V | 19855 | 214 | 290 |
| BG1.305 | 305 | V | 19855 | 309 | 213 |
| BG1.310 | 310 | V | 20848 | 258 | 21 |
| BG1.315 | 315 | V | 20848 | 162 | 196 |
| BG1.320 | 320 | VI | 20848 | 114 | 236 |
| BG1.325 | 325 | VI | 19855 | 370 | 206 |
| BG1.330 | 330 | VI | 19855 | 119 | 203 |
| BG1.335 | 335 | VI | 19855 | 465 | 277 |
| BG1.340 | 340 | VI | 19855 | 49 | 216 |
| BG1.345 | 345 | VI | 19855 | 44 | 39 |
| BG1.350 | 350 | VI | 19855 | 0 | 13 |
| BG1.355 | 355 | VI | 19855 | 3 | 214 |
| BG1.360 | 360 | VI | 19855 | 400 | 207 |
Phytolith and pollen microfossils were identified using a binocular light microscope at 400x-1000x. Phytolith nomenclature and categories follow the International Code for Phytolith Nomenclature (ICPT 2019), but we tried specifically to create sample categories consistent with Bremond et al.’s (2005, 2008) assessment of phytoliths from surface samples across West Africa (Table 2).
| AFFILIATION | TYPE | GROUP | Bremond_et_al_2005 | |
|---|---|---|---|---|
| 9 | DICOTYLEDONS | SPH_ORN | WOODY PLANTS | Rough spherical |
| 10 | DICOTYLEDONS | SPH_CMP | WOODY PLANTS | Non-diagnostic |
| 11 | ARECACEAE | SPH_ECH | PALMS | Crenate spherical |
| 12 | MONOCOTYLEDONS* | ACU_BUL | MONOCOTS | Point-shaped |
| 13 | POACEAE_CYPERACEAE | BUL_FLA | MONOCOTS | Fan-shaped |
| 14 | POACEAE | PAP | GRASSES | Cone-shaped |
| 15 | POACEAE | RON | GRASSES | Non-diagnostic |
| 16 | POACEAE | BIL | GRASSES | Dumbell |
| 17 | POACEAE | CRO | GRASSES_PANICOID | Cross |
| 18 | POACEAE | SAD | GRASSES_CHLORIDOID | Saddle |
| 19 | POACEAE* | ELO_SIN | ELONGATE_GRASSES | Non-diagnostic |
| 20 | POACEAE* | ELO_DET | ELONGATE_GRASSES | Non-diagnostic |
| 21 | POACEAE* | ELO_DEN | ELONGATE_GRASSES | Non-diagnostic |
| 22 | VARIOUS | SPH_PSI | UNDIFF | Smooth spherical |
| 23 | VARIOUS | ELO_ENT | UNDIFF | Non-diagnostic |
| 27 | VARIOUS | BLO | UNDIFF | Fan-shaped |
| 28 | VARIOUS | UNDIFF | CORE_DETAILS | Non-diagnostic |
Phytolith frequencies were quantified by counting all phytoliths until a sample size of 200 diagnostic phytoliths was reached (see Bremond et al. 2005, 2008). Concentrations of phytoliths and pollen were established by tracking Lycopodium spores encountered during analysis. Samples where 200 Lycopodium were encountered before 50 identifiable phytoliths were considered unreliable. Although the number of diagnostic pollen sampled does reach 100 in some samples, the number of damaged, fragmented, and indeterminate types is too high to pursue reliable statistical reconstructions with the pollen data. In general, the patterns in microfossil deposition/preservation closely follow the site’s sedimentology.
Figure 1. Phytolith and Pollen Recovery and Preservation
Phytoliths from the Bargny 1 samples show a strong representation of Chloridoideae and Panicoideae phytolith types (BIL and CRO, respectively) as well as types belonging to undifferentiated monocots (ACU_BUL, BUL_FLA, and RON). The frequency of SPH_ECH phytoliths is high in some of the lowermost samples, but we must use caution in interpreting this morphotype as it overlaps with sponge spicules common in coastal zones (ICPN 2009). While this type was tracked during analysis, this type is excluded from the statistical analyses. Samples with excessive SPH_PSI and SPH_ECH tend to be the most weathered and have the poorest preservation. These are excluded from futher analysis.
Figure 2. Phytolith results as a percent of the identifiable phytolith sum.
Identifiable pollen was preserved in the samples and provides some insights into the kinds of vegetation cover that grew at the site during the major depositional phases. The lowermost samples are characterized by smaller pollen counts and low concentrations which steadily rise up to about 250 cm, where the samples yielded high pollen counts and a greater concentration of pollen. For much of the middle section of the deposits, the pollen are often damaged and although identified types are high, these tend to be types that are easily distinguished (Poaceae/Amaranthaceae). There’s a modest improvement in pollen preservation/concentration between 60-90 cm as well. What we do see in these samples are examples of types introduced by trade-wind activity (Olea-type & Pinus), riparian/estuarine trees/shrubs (Avicennia, Syzygium), and regional Sudanian woodland pollen types (Acacia, Celtis, Trema).
Figure 3. Pollen results as a percent of the identifiable phytolith sum.
Figure 4. Phytolith and pollen percent by vegetation type.
Studies of surface soil phytoliths conducted by Bremond and colleagues (2005, 2008) provide an important resource for evaluating archaeological phytolith aseemblages. We use Principal Component Analysis and Minimum Square-Chord Distances to compare the phytolith spectra and to gain insights into the range of environments represented at Bargny 1. PCA was chosen because of the “predict” functionality in R, which is not available for other types of factor analysis (constrained correspondence, etc.). To look for changes in rainfall or the boundaries of major vegetation zones, we applied MSCD to establish the most similar sets of modern samples and plot their relevant climatic (rainfall) or geographic (latitude) representation across the depositional sequence.
Figure 6. PCA of modern soil sample (Bremond et al. 2005; 2008 - left) and Bargny 1 results plotted using the same eigenvalues.
PCA shows the influence of rainfall on Bremond et al.s (2005, 2008) phytolith spectra. However, the individual vegetation zones are only generally resolved using these vectors. Ellipses (65% and 95%) drawn around the important vegetation types near Bargny (Sahelian, Sudanian, and Saharan) to show the degree of overlap. The archaeological samples, when plotting using the eigenvectors from the original PCA, show a tight clusering with positive values on component 1 and low scores on component 2. Most of the samples fall within the 95% ellipse for Sahelian woodlands, but stratum 3 and 1 both yielded more negative component 1 loadings, indicating drier conditions.
Figure 7. Plots comparing Bargny samples with surface samples (Bremond et al. 2005) using square-chord distances of scaled (z-score) diagnostic phytolith results. The five best matches are plotted by their current precipitation regime per sampled depth.
We can also compare the values for indices used by Bremond et al. (2005, 2008) to evaluate aridity/savanna type (Iph) and grass water stress (Fs). These values do a good job of discriminating the boundaries between the Sudanian and Guinean zones (Iph values) and the Sahelian and Saharan zones (Fs). These values allow us to look for potential turnover betwen major vegetation formations.
Figure 4. Comparison of long vs. short grass savanna index (Iph, x-axis) and Fan-shaped index (Fs, y-axis) results for Bargny samples (green and blue squares) with Bremond et al.’s (2005) results from West African surface samples
To create some insights into the scale and direction of past vegetation change represented in the Bargny 1 deposits, we plot the results for four different indices, grouping their results by stratum (Figure 6.). The indices include Fs, Iph, Grass-Amaranthaceae pollen, and wetland/riparian/mangrove pollen. As expected, the Grass/Amaranthaceae ratio is not especially informative, but the other indices show a clear turnover after stratum 4, when the depositonal environment shifts from near-coastal estuarine conditions to halophytic dry coastal plain.
Figure 6. Boxplots of Phytolith indices and grouped pollen results by stratum.
We further synthesize the results by plotting the Fs, Iph, and estuarine indices by stratum showing both the distribution of sample results (points) and their collective behavior (kernel density plots/color scheme).
Figure 7. Synthetic results showing wetland, water stress, and precipitation proxies with kernel density plots for each stratum